Publications:State of the art prediction of HIV-1 protease cleavage sites
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| Title | State of the art prediction of HIV-1 protease cleavage sites |
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| Author | |
| Year | 2015 |
| PublicationType | Journal Paper |
| Journal | Bioinformatics |
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| Conference | |
| DOI | http://dx.doi.org/10.1093/bioinformatics/btu810 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:768706 |
| Abstract | Motivation: Understanding the substrate specificity of HIV-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved. Results: The linear support vector machine with orthogonal encod-ing is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor ser-vices. It is also found that schemes using physicochemical proper-ties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed. Availability: The data sets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. © 2014 The Author. |
